The area of business operations monitoring and management is rapidly gaining importance both in industry and in academia. Performance reporting tools essentially leverage system and network monitoring applications, data warehousing and on-line analytical processing applications to perform on-line analysis of business operations. Such analysis results in charts and tables from which users can identify the state of a system and what is happening in that system at a high level and then access further information that provides greater detail of the business operations.
Whilst such systems provide value, there is a large gap between what is presently available and what users wish to access in the area of business operations monitoring and management. Only recently has business level operation analysis become a key component of a company's IT infrastructure and therefore related technologies are still immature.
Business analysts tend to think of the way business operations are performed in terms of high level business processes, termed abstract, as they are abstracted versions of the actual processes being executed by the company's IT infrastructure. At present there is no manner in which an analyst can draw such abstract processes and use them to analyze and report on business operations. Furthermore, defining metrics of interest and reporting against these metrics requires a significant coding effort. Even then, only simple charts plotting historical or present metric values are available.
No system provides the facility for easily defining metrics over process execution data, for providing users with explanations for why a certain metric has a certain value and for predicting the future value of a metric on a process, all of which are required by business analysts. Furthermore there is no automated support for modifying the abstract business processes to improve critical metrics and there is no support for understanding the business impact of system failures.
Prior art solutions to the above have required very intensive manual labor, for example in terms of the effort of programming and coding at each step. A large amount of coding has been required to define metrics, to perform metric analysis, to perform predictions and to simulate and optimize the process. None of these steps has ever been performed together in an automated fashion that has been easy to use at every process. A wide variety of both products and solutions may have been proposed to satisfy one or more of these requirements but none has provided a solution to providing every process in an automated fashion.